Intelligent manufacturing equipment control method and system based on large diffusion model

Abstract

The invention relates to the technical field of manufacturing equipment control, in particular to an intelligent manufacturing equipment control method and system based on a large diffusion model. The method comprises the following steps: acquiring multi-mode type data of the intelligent manufacturing equipment; the method comprises the following steps: performing time series data acquisition based on multi-modal type data of intelligent manufacturing equipment to generate an initial multi-modal data set; performing operation mode fusion on the initial multi-mode data set to generate manufacturing equipment multi-mode operation data; equipment virtual operation processing is carried out on the manufacturing equipment multi-mode operation data based on a preset diffusion large model, and an equipment operation virtual state distribution diagram is generated; and performing physical constraint optimization on the initialized diffusion large model based on the equipment operation virtual state distribution diagram, and generating an equipment physical characteristic matching diffusion model. According to the method, through combination of the diffusion large model, multi-modal data fusion, incremental learning and differential privacy protection, the control accuracy and adaptability of the intelligent manufacturing equipment are improved.

Description

Technical Field

[0001] The present invention relates to the field of equipment control technology, and in particular to an intelligent manufacturing equipment control method and system based on a diffusion large model. Background Art

[0002] In the early days, the control methods of intelligent manufacturing equipment mainly relied on traditional PLC (programmable logic controller) and DCS (distributed control system). These methods completed the automated operations in the production process through predetermined programs and fixed control logic, and were suitable for simple and repetitive manufacturing tasks. However, with the increasing complexity of manufacturing processes, traditional control methods gradually showed the problem of insufficient flexibility. With the development of artificial intelligence technology, especially the application of deep learning and machine learning algorithms, the control of intelligent manufacturing equipment has begun to develop towards higher automation and intelligence. Machine learning algorithms can make predictions and decisions based on historical data, optimize production processes, and improve equipment operation efficiency and accuracy. In recent years, the diffusion model has begun to be used in intelligent manufacturing as a generative model. The diffusion model can generate complex structures and patterns in high-dimensional space, and is suitable for the generation and optimization of multimodal data such as images and videos. In the field of intelligent manufacturing, the control method based on the diffusion model can achieve more accurate equipment status prediction, fault diagnosis and real-time optimization, especially in the multidimensional data fusion and anomaly detection of complex systems. Great potential. However, in current traditional intelligent manufacturing, equipment control strategies often rely on empirical rules or prior knowledge, which makes it difficult to achieve personalized optimization and long-term adaptation. At the same time, static control strategies are usually adopted, which makes it difficult to cope with real-time and dynamic changes in equipment operation, resulting in low accuracy and adaptability of intelligent manufacturing equipment control. Summary of the invention

[0003] Based on this, it is necessary to provide a control method and system for intelligent manufacturing equipment based on a diffusion large model to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for controlling intelligent manufacturing equipment based on a diffusion large model is provided, the method comprising the following steps: Step S1: Acquire multimodal data of intelligent manufacturing equipment; perform time series data collection based on the multimodal data of intelligent manufacturing equipment to generate an initial multimodal data set; perform operation mode fusion on the initial multimodal data set to generate multimodal operation data of manufacturing equipment; Step S2: Based on the preset diffusion macro model, the multimodal operation data of the manufacturing equipment is processed for virtual operation to generate a virtual state distribution map of the equipment operation; based on the virtual state distribution map of the equipment operation, the initialized diffusion macro model is optimized for physical constraints to generate a diffusion model matching the physical characteristics of the equipment; based on the virtual state distribution map of the equipment operation, the equipment physical characteristics matching diffusion model is verified and tested for multiple rounds of models to generate an optimized set of equipment control strategies; Step S3: Perform state matching analysis on the multimodal operation data of manufacturing equipment according to the candidate set of equipment control strategies to generate a dynamic control strategy; perform multi-equipment collaborative optimization on intelligent manufacturing equipment based on the dynamic control strategy to generate dynamic equipment collaborative optimization data; perform execution effect collection and analysis on the dynamic equipment collaborative optimization data to generate an incremental learning data set; perform diffusion model incremental learning and continuous optimization on the equipment physical characteristic matching diffusion model through the incremental learning data set, thereby generating a long-term optimization control matrix for the equipment; Step S4: Perform access control on the equipment long-term optimization control matrix and the equipment physical characteristics matching diffusion model to generate equipment control differential privacy protection data; perform integrity verification on the equipment control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access strategy of the equipment long-term optimization control matrix according to the security threat monitoring log to generate a final security optimization strategy to execute intelligent manufacturing equipment control operations.

[0005] The present invention can comprehensively and accurately reflect the operating status of the equipment by acquiring multimodal data (such as temperature, vibration, sound, image, etc.) from multiple sensors. The fusion of multimodal data improves the accuracy of equipment fault detection and status monitoring, and avoids information bias caused by a single data source. Time series data acquisition enables the operation process of the equipment to be dynamically tracked, ensuring timeliness and integrity. Through the fusion of operating modes, the characteristics of different data sources can be integrated to form more comprehensive manufacturing equipment operation data, which helps to improve the quality of subsequent model training and analysis. Equipment virtual operation processing can generate a virtual state distribution map of the equipment, providing a data basis for further physical property analysis and optimization. The physical constraint optimization of the diffusion large model can make the generated model more consistent with the working characteristics of the actual equipment, avoiding the problem of mismatch between the model and the physical characteristics of the equipment in the traditional method. Through multiple rounds of model verification and testing, the accuracy and reliability of the equipment control strategy are ensured, which helps to optimize the control effect and improve the equipment performance. The dynamic control strategy is adjusted according to the real-time operation data of the equipment, ensuring the real-time adaptability and optimization capability of the equipment, and can cope with changes in the equipment under different operating conditions. Multi-device collaborative optimization can simultaneously optimize the working status of multiple devices, improve production efficiency, reduce conflicts between devices, and enhance the collaborative ability of the entire manufacturing system. Through incremental learning, the control model of the device is continuously updated and optimized according to the new execution data, which avoids outdated control strategies and helps to achieve continuous improvement and optimization of the equipment in long-term operation. Differential privacy protection ensures the security of sensitive data in the intelligent manufacturing process, protects the core technology and trade secrets of the enterprise, and prevents data leakage or abuse. Integrity verification of device control data helps to discover potential data tampering risks and ensure the reliability of device control data. Based on the dynamic adjustment of storage and access strategies based on security threat monitoring logs, security strategies can be optimized in real time according to changes in security threats, the overall anti-attack capability of the system can be improved, and the long-term safe and stable operation of intelligent manufacturing equipment can be ensured. Therefore, the present invention improves the accuracy and adaptability of intelligent manufacturing equipment control by combining diffusion large models, multimodal data fusion, incremental learning and differential privacy protection.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire multimodal type data of intelligent manufacturing equipment; Step S12: extracting high-frequency sensor signals, operation logs, images and video stream data from the multimodal data of intelligent manufacturing equipment, and performing time series data collection on the high-frequency sensor signals, operation logs, images and video stream data to generate an initial multimodal data set; Step S13: performing time alignment on the initial multimodal data set to generate a multimodal alignment data set for manufacturing equipment; performing inter-modal correction on the multimodal alignment data set for manufacturing equipment to generate a multimodal fusion data set; Step S14: extracting cross-modal features of the multimodal fusion data set to obtain cross-modal feature data of manufacturing equipment, wherein the cross-modal feature data of manufacturing equipment includes frequency domain features, spatial features, and semantic features; fusing the cross-modal feature data of manufacturing equipment to generate a multimodal feature tensor; Step S15: Decompose and compress the multimodal feature tensor to generate multimodal operation data of the manufacturing equipment.

[0007] The present invention can provide a comprehensive and rich data foundation for subsequent analysis by acquiring and extracting multimodal data (such as high-frequency sensor signals, operation logs, images and video stream data) and performing time series acquisition, covering the multi-dimensional status and operation information of manufacturing equipment. Through time series alignment and inter-modal correction, multimodal data can maintain consistency in time and space dimensions, effectively reducing analysis errors caused by different modal characteristics or asynchronous data sources, and enhancing data reliability and accuracy. Cross-modal features such as frequency domain features, spatial features and semantic features are extracted, and multimodal feature tensors are generated through fusion processing to provide high-level feature expressions for subsequent analysis. This cross-modal feature extraction method can better capture the potential correlation between data and improve information utilization efficiency. Decomposing and compressing multimodal feature tensors can not only reduce data redundancy and storage space requirements, but also extract refined multimodal operation data, which is convenient for subsequent intelligent analysis and modeling, and improves data processing efficiency. The multimodal operation data of manufacturing equipment finally generated provides accurate data support for intelligent manufacturing tasks such as equipment monitoring, anomaly detection, and optimization control, which helps to improve the operation efficiency, stability and production quality of equipment.

[0008] Preferably, step S2 comprises the following steps: Step S21: Initializing a preset diffusion macromodel based on the multimodal operation data of the manufacturing equipment to generate an initialized diffusion macromodel; inputting the multimodal operation data of the manufacturing equipment into the initialized diffusion macromodel to perform equipment virtual operation processing to generate a virtual state distribution diagram of the equipment operation; Step S22: Based on the equipment operation virtual state distribution diagram, the initialized diffusion model is optimized by physical constraints to generate a diffusion model matching the equipment physical characteristics; the multi-modal operation data of the manufacturing equipment is trained multiple times by using the equipment physical characteristics matching diffusion model to generate a candidate set of equipment control strategies; Step S23: Model verification and testing are performed on the equipment physical characteristic matching diffusion model through the equipment control strategy candidate set to generate an optimized equipment control strategy set.

[0009] The present invention generates a virtual state distribution diagram of equipment operation by inputting the multimodal operation data of manufacturing equipment into the initialized diffusion model. This data-driven virtual operation can accurately simulate the operation state of the equipment under different conditions, laying the foundation for subsequent optimization. By introducing physical constraint optimization, the real physical characteristics of the equipment are embedded in the diffusion model, so as to generate a diffusion model matching the physical characteristics of the equipment. This process can reduce model deviation, enhance the consistency of the results with the actual equipment behavior, and improve the credibility and interpretability of the model. The optimized diffusion model is used to train the multimodal operation data for multiple rounds to generate a candidate set of equipment control strategies. This iterative training method fully explores the potential characteristics of the data, helps to generate diversified control strategies, and supports the needs of complex manufacturing scenarios. By verifying and testing the candidate set of equipment control strategies, an optimized set of equipment control strategies is finally generated. This rigorous verification process ensures the reliability and applicability of the control strategy, and helps to improve the efficiency and stability of equipment operation. The optimized set of equipment control strategies finally generated provides precise guidance for the intelligent control of equipment operation, can realize dynamic optimization of the manufacturing process, reduce energy consumption, improve production capacity, and help the comprehensive upgrade of the intelligent manufacturing system. By introducing physical constraints and virtual operation into the model, the diffusion model can not only adapt to the current equipment, but also has good scalability and can be applied to optimization and control tasks of other similar equipment or scenarios.

[0010] Preferably, performing physical constraint optimization on the initialized diffusion model based on the equipment operation virtual state distribution diagram includes: Extract key operating state parameters from the equipment operation virtual state distribution diagram data to generate equipment operation characteristic matrix data, where the key operating state parameters extracted include operating speed, temperature, and vibration frequency; perform physical constraint modeling on the equipment operation characteristic matrix data to generate physical characteristic constraint function set data; Perform model constraint embedding processing on the physical characteristic constraint function set data to generate preliminary constraint diffusion model data; perform iterative training on the preliminary constraint diffusion model data to generate equipment physical characteristic matching diffusion model training data; Feedback optimization is performed on the equipment physical characteristic matching diffusion model training data to generate the equipment physical characteristic matching diffusion model.

[0011] The present invention can fully reflect the core operating characteristics of the equipment by extracting key operating state parameters, including operating speed, temperature and vibration frequency, from the equipment operation virtual state distribution diagram data. These key parameters provide basic data for subsequent physical constraint modeling and optimization, ensuring the accuracy and pertinence of the optimization process. The extracted operating state parameters are converted into equipment operation characteristic matrix data, providing structured data support for physical constraint modeling. This matrix form of characteristic data helps to better understand the behavior mode of the equipment under different operating conditions and helps to achieve multi-dimensional optimization. By performing physical constraint modeling on the equipment operation characteristic matrix data and generating physical characteristic constraint function set data, this process can combine the actual physical laws with the equipment operation data, enhance the matching degree between the model and the actual situation, ensure that the model can follow the physical constraints when executing, and improve the feasibility. The physical characteristic constraint function set data is subjected to model constraint embedding processing to generate preliminary constraint diffusion model data. The embedding of constraint conditions ensures that the diffusion model can not only work in theory, but also play a role in actual equipment operation, prevents the model from producing prediction results that are not in line with reality, and improves the stability and availability of the model. By iteratively training the preliminary constrained diffusion model data, the equipment physical characteristics matching diffusion model training data is generated. This process can continuously optimize the model and improve the model's adaptability and prediction accuracy to the actual equipment operating status. Iterative training ensures that the model can effectively predict and control under different equipment states. Finally, the equipment physical characteristics matching diffusion model training data is feedback optimized to generate the final equipment physical characteristics matching diffusion model. Through the feedback optimization process, the model continuously adapts to equipment changes and different operating environments, and finally achieves higher accuracy and stability, which can better support the intelligent control and optimized operation of the equipment. Through the generated physical characteristics matching diffusion model, a more accurate equipment control strategy can be implemented, thereby improving equipment operating efficiency, reducing failures, and improving the stability of the manufacturing process. The model can adapt to changes in equipment status in real time, providing strong support for intelligent manufacturing systems.

[0012] Preferably, step S3 comprises the following steps: Step S31: performing state matching analysis on multi-modal operation data of manufacturing equipment according to the equipment control strategy candidate set to generate a dynamic control strategy; deconstructing a control instruction set based on the dynamic control strategy to generate an equipment control instruction set; Step S32: using the device control instruction set to operate the intelligent manufacturing device and collect feedback data to generate a control execution feedback matrix; adaptively updating the dynamic control strategy in real time through the control execution feedback matrix to generate an adaptive dynamic control strategy; Step S33: performing multi-device collaborative optimization on intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate dynamic equipment collaborative optimization data; performing execution effect collection and analysis on the dynamic equipment collaborative optimization data to generate an incremental learning data set; Step S34: Perform incremental learning and continuous optimization of the diffusion model by matching the physical characteristics of the equipment with the diffusion model through the incremental learning data set, thereby generating a long-term optimization control matrix for the equipment.

[0013] The present invention generates a dynamic control strategy by performing state matching analysis on multimodal operation data based on a candidate set of device control strategies. This method can generate an optimized control strategy in real time according to the state change of the device, ensuring that the device can obtain the most suitable control instructions under different working conditions, thereby improving the operating efficiency and stability of the device. The control instruction set deconstruction process converts the dynamic control strategy into an executable device control instruction set, so that the control strategy can be directly applied to the device operation. The deconstructed instruction set can simplify the device operation process and improve the efficiency and accuracy of instruction execution. The dynamic control strategy is adaptively updated in real time through the control execution feedback matrix to ensure that the control strategy can be optimized and adjusted according to the actual operation feedback. This real-time optimization enables the device to continuously adapt to changes in the working environment, reduce errors and failures, and thus improve the stability and controllability of the system. Based on the adaptive dynamic control strategy, multiple devices are collaboratively optimized to generate dynamic device collaborative optimization data. Through the collaborative optimization between multiple devices, the overall production efficiency can be improved, the conflicts and imbalances between devices can be reduced, and the manufacturing process can be more coordinated and smooth. This step further improves the integration and intelligence level of the intelligent manufacturing system. By performing effect collection and analysis, an incremental learning data set is generated to provide a basis for continuous improvement of the device physical property matching diffusion model. Incremental learning can gradually integrate new data and feedback, continuously improve the accuracy and adaptability of the model, and effectively avoid the obsolescence problem of traditional models. Through incremental learning, the diffusion model is continuously optimized, and finally the long-term optimization control matrix of the equipment is generated. This process ensures that the control strategy of the equipment can maintain an optimized state for a long time, gradually adapt to the operation trend of the equipment and changes in the external environment, and finally achieve continuous performance improvement and efficient operation of the equipment. Through feedback data, dynamic adjustment and incremental learning, the entire step S3 not only improves the optimization capability of a single device, but also makes the entire intelligent manufacturing system have stronger adaptability and long-term optimization capabilities, and can continuously learn and optimize control strategies to support more intelligent and efficient production processes.

[0014] Preferably, performing state matching analysis on multimodal operation data of manufacturing equipment according to the equipment control strategy candidate set includes: Screening the operation status rules of the equipment control strategy candidate set to obtain the equipment control operation status rules; performing a comparative analysis of the real-time operation characteristics of the multi-modal operation data of the manufacturing equipment item by item according to the equipment control operation status rules, thereby generating feature comparative analysis data; Calculate the cosine similarity of the feature comparison analysis data to generate a state matching score matrix; extract the highest score from the state matching score matrix to obtain the optimal state matching score data; perform strategy optimization processing on the device control strategy candidate set based on the optimal state matching score data to generate the optimal control strategy set data; Based on the timeline of the multimodal operation data of the manufacturing equipment, the optimal control strategy set data is logically integrated and processed to generate dynamic control strategy data.

[0015] The present invention can extract the rules most relevant to the operation state of the device by screening the operation state rules of the device control strategy candidate set. This process ensures that the selection of the control strategy is closely matched with the current operation state of the device, thereby improving the accuracy and pertinence of the control decision. According to the device control operation state rules, the multimodal operation data is compared and analyzed item by item to generate feature comparison analysis data. This process can accurately reflect the fit between the current state of the device and each control strategy through real-time comparison of various operation characteristics of the device, and provides a detailed basis for subsequent state matching and strategy optimization. The feature comparison analysis data is calculated by cosine similarity to generate a state matching score matrix. By calculating the cosine similarity, the similarity between the device operation characteristics and the control strategy can be quantified, thereby achieving accurate matching. Cosine similarity is a commonly used and efficient similarity calculation method that can effectively reduce errors and improve the accuracy of matching. The state matching score matrix is ​​extracted with the highest score to obtain the optimal state matching score data. This process can quickly screen out the strategy that best matches the current device state from multiple control strategies, ensure that the optimal strategy is selected at the most appropriate time, and improve the accuracy and efficiency of device operation. According to the optimal state matching score data, the control strategy candidate set is optimized to generate the optimal control strategy set data. This optimization process ensures the quality of the control strategy set and excludes inapplicable or inappropriate strategies to ensure that the selected strategy can maximize the performance of the equipment. Based on the timeline of the multimodal operation data of the equipment, the optimal control strategy set is logically integrated to generate dynamic control strategy data. This process not only takes into account the real-time operation of the equipment, but also optimizes the order and logic of strategy execution according to the time series, thereby generating dynamic control strategy data that can cope with the constant changes in the equipment state. Through real-time matching, optimization and integration of control strategies, the system can dynamically adjust the control strategy according to the specific operation state of the equipment. This process greatly improves the adaptability and response speed of the equipment, helps the equipment to always maintain the best operation state under different operating environments and conditions, thereby improving the overall operation efficiency and intelligence level. Combined with feature comparison analysis, cosine similarity calculation and optimal strategy generation, the system can make accurate control decisions based on the real-time operation data of the equipment, which enables the equipment to achieve efficient and intelligent automatic control in complex environments, further promoting the realization of intelligent manufacturing.

[0016] Preferably, step S33 includes the following steps: Step S331: performing inter-device task decomposition processing on the intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate equipment task allocation matrix data, wherein the inter-device task decomposition processing includes load capacity decomposition and resource occupancy decomposition; Step S332: Coordinate the equipment operation parameters of the equipment task allocation matrix data to generate multi-equipment collaborative operation parameter set data; Step S333: dynamically executing simulation processing on the multi-device collaborative operation parameter set data to generate dynamic device collaborative optimization data; extracting key collaborative indicators from the dynamic device collaborative optimization data to obtain multi-device key collaborative indicators, wherein the multi-device key collaborative indicators include inter-device delay, resource conflict or energy consumption deviation; Step S334: collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set.

[0017] The present invention performs task decomposition processing on the equipment through an adaptive dynamic control strategy, and generates equipment task allocation matrix data. This process ensures the reasonable allocation of tasks between equipment, takes into account the load capacity and resource occupation of each equipment, avoids excessive load and resource waste, and improves the overall task execution efficiency of the system. The equipment operating parameters are coordinated through the equipment task allocation matrix data to generate multi-equipment collaborative operation parameter set data. This process promotes the coordination between equipment. By optimizing the operating parameters, multiple devices can collaborate efficiently when working together, reduce conflicts between devices, and improve overall work efficiency. By dynamically executing simulation processing on the multi-equipment collaborative operation parameter set data, dynamic equipment collaborative optimization data is generated. This simulation processing helps predict the execution effect of collaboration between different equipment and promptly discover potential optimization space. Through dynamic simulation, optimization and adjustment can be performed before actual operation to avoid instability and inefficiency in system operation. By extracting key collaborative indicators from dynamic equipment collaborative optimization data, step S333 can generate key collaborative indicators of multiple devices, such as delay between devices, resource conflicts or energy consumption deviations, etc. These indicators provide a quantitative basis for collaborative optimization between devices, which helps to monitor and adjust the collaborative work efficiency in actual operation to ensure the efficient operation of the equipment. By collecting and analyzing the execution effect, an incremental learning data set is generated. This process can collect execution effect data in real time, provide data support for incremental learning, and continuously optimize the collaborative control strategy. Through incremental learning, the system can dynamically adjust the optimization plan according to the latest execution data, improve the system's adaptive ability and decision-making efficiency. Through means such as equipment task decomposition, collaborative operation parameter optimization and incremental learning, the entire system can efficiently coordinate the operation of each device and reduce problems such as resource conflicts, delays and energy consumption deviations. Efficient collaboration between devices will significantly improve the overall work efficiency and production capacity of the intelligent manufacturing system. Through the generation of incremental learning data sets, the system can continuously optimize according to real-time feedback, thereby achieving continuous improvement of equipment collaborative control strategies. Incremental learning not only improves the system's adaptive ability, but also ensures the long-term effectiveness of equipment collaborative optimization.

[0018] Preferably, step S334 includes the following steps: Step S3341: collect multi-dimensional execution effect data of dynamic device collaborative optimization data to generate an initial data set of device collaborative execution effect; perform expected value comparison analysis on the initial data set of device collaborative execution effect to generate execution deviation data; Step S3342: comparing the execution deviation data with a preset standard execution deviation threshold, and when the execution deviation data is greater than or equal to the preset standard execution deviation threshold, performing attribution analysis and optimization feedback on the execution deviation data to generate execution optimization feedback data; Step S3343: When the execution deviation data is less than a preset standard execution deviation threshold, incremental learning data is constructed for the execution deviation data to generate an incremental learning data set.

[0019] The present invention generates an initial data set of equipment collaborative execution effect through multi-dimensional data collection. This process ensures that the execution effect of equipment collaborative optimization is recorded comprehensively and accurately. Next, the expected value comparison analysis is performed to generate execution deviation data, which helps to find the gap between the actual execution effect and the expected target and provide a basis for subsequent optimization. When the execution deviation data is greater than or equal to the preset standard execution deviation threshold, the execution deviation data will be subjected to attribution analysis and optimization feedback. Through attribution analysis, the specific source of the deviation can be located and the corresponding links can be optimized and adjusted. Optimizing feedback data can help improve the collaborative efficiency between devices and reduce unnecessary resource consumption or delays. When the execution deviation data is less than the preset threshold, incremental learning data construction will be performed to generate incremental learning data sets. This process ensures that the system can continue to learn according to the real-time execution situation, generate new learning data, and continuously optimize the control strategy. Incremental learning not only improves the adaptive ability of the system, but also enables the intelligent manufacturing system to remain efficient and stable in long-term operation. The incremental learning mechanism enables the system to dynamically adjust the strategy according to the latest execution data, thereby continuously optimizing the equipment collaborative control scheme. Through continuous learning and adjustment, the system can adapt to complex environmental changes, optimize the collaborative work efficiency of each device, and ensure long-term efficient operation. Through real-time monitoring and feedback of execution deviations, the system can promptly detect and handle potential problems, thereby improving the responsiveness of the intelligent manufacturing system to changing environments. At the same time, the mechanism of deviation analysis and optimization feedback also ensures the stability of the system and reduces the impact of emergencies on system operation. Through detailed analysis and optimization of execution deviations, the accuracy of coordination between devices has been significantly improved. Through attribution analysis and optimization feedback, task execution between devices has become more accurate, reducing resource conflicts, delays and other problems in the coordination process, thereby improving overall work efficiency.

[0020] Preferably, step S4 comprises the following steps: Step S41: Distributed encrypted storage is performed on the equipment long-term optimization control matrix and the equipment physical characteristic matching diffusion model to generate an encrypted storage mapping table; access control is performed on the encrypted storage mapping table based on a differential privacy protection mechanism to generate equipment control differential privacy protection data; Step S42: Perform integrity check on the device control differential privacy protection data and generate a security threat monitoring log; dynamically adjust the storage and access strategy of the device long-term optimization control matrix based on the security threat monitoring log, and generate a final security optimization strategy to execute intelligent manufacturing equipment control operations.

[0021] The present invention distributes and encrypts the device long-term optimization control matrix and the device physical characteristics by matching the diffusion model. This method can effectively prevent single point failures and unauthorized access, and improve the overall security and disaster recovery capability of data storage. The encrypted storage mapping table provides support for rapid positioning and management of data, while ensuring that the security of stored data will not be affected by the leakage of the mapping relationship. The differential privacy protection mechanism provides an effective privacy protection method for access to device control data, which can minimize the risk of privacy leakage while using data. The device control differential privacy protection data limits the exposure of sensitive data through access control, ensuring that only authorized users or devices can access the device long-term optimization control matrix. The integrity check provides security for the device control differential privacy protection data, and can detect whether the data is tampered with or attacked during storage and transmission. The dynamic adjustment of storage and access strategies based on the security threat monitoring log ensures that the system can optimize the strategy in time according to the current security status, and improves the execution reliability and security of the device control operation. The security threat monitoring log provides real-time recording and analysis capabilities for potential threats, which helps to quickly discover and locate security risks. By dynamically adjusting the storage and access strategies, external or internal security threats can be responded to quickly to prevent data leakage or system intrusion. The final security optimization strategy integrates the advantages of distributed encrypted storage, differential privacy protection, integrity verification and dynamic adjustment strategies, providing efficient and secure execution guarantees for device control operations. The system can automatically adapt strategies according to operational requirements and security situations, so that device control operations can maintain stability and efficiency in long-term operation. The combination of differential privacy protection mechanism and dynamic optimization strategy minimizes the impact on system performance while improving privacy protection. The dynamic adjustment strategy can intelligently allocate resources according to the threat level to ensure a balance between privacy protection and system performance. The application of distributed storage and dynamic optimization strategies provides a basis for the efficient management of the long-term optimization control matrix of the device. This method not only ensures the long-term security of the data, but also facilitates subsequent data analysis and optimization.

[0022] In this specification, a smart manufacturing equipment control system based on a diffusion large model is provided, which is used to execute the above-mentioned smart manufacturing equipment control method based on a diffusion large model. The smart manufacturing equipment control system based on a diffusion large model includes: The modal fusion module is used to obtain multi-modal data of intelligent manufacturing equipment; perform time series data collection based on the multi-modal data of intelligent manufacturing equipment to generate an initial multi-modal data set; perform operation modal fusion on the initial multi-modal data set to generate multi-modal operation data of manufacturing equipment; The virtual operation module is used to perform virtual operation processing on the multimodal operation data of the manufacturing equipment based on the preset diffusion macro model, and generate a virtual state distribution map of the equipment operation; perform physical constraint optimization on the initialized diffusion macro model based on the equipment operation virtual state distribution map, and generate a diffusion model matching the equipment physical characteristics; perform multiple rounds of model verification and testing on the equipment physical characteristics matching diffusion model based on the equipment operation virtual state distribution map, and generate an optimized set of equipment control strategies; The equipment collaborative control module is used to perform state matching analysis on the multimodal operation data of manufacturing equipment according to the candidate set of equipment control strategies to generate dynamic control strategies; perform multi-equipment collaborative optimization of intelligent manufacturing equipment based on dynamic control strategies to generate dynamic equipment collaborative optimization data; perform execution effect collection and analysis on dynamic equipment collaborative optimization data to generate incremental learning data sets; perform diffusion model incremental learning and continuous optimization on the equipment physical characteristics matching diffusion model through the incremental learning data sets, thereby generating a long-term optimization control matrix for the equipment; The secure storage module is used to perform access control on the equipment long-term optimization control matrix and the equipment physical characteristics matching diffusion model, generate equipment control differential privacy protection data; perform integrity verification on the equipment control differential privacy protection data, and generate security threat monitoring logs; dynamically adjust the storage and access strategies of the equipment long-term optimization control matrix according to the security threat monitoring logs, and generate the final security optimization strategy to execute intelligent manufacturing equipment control operations.

[0023] The beneficial effect of the present invention is that by fusing data of multiple sensor types (such as temperature, vibration, sound, etc.), various types of equipment operation information can be fully captured, and the accuracy and completeness of the equipment status can be improved. Time series data acquisition ensures real-time monitoring and long-term tracking of the equipment operation status, which helps to identify potential faults and provide early warnings. Through operation mode fusion, data features from different sensors can be effectively integrated to generate high-dimensional multi-modal operation data, providing a high-quality data basis for subsequent analysis and modeling. Through virtual operation processing, the operation status of the equipment can be simulated without actual equipment operation. This helps to verify and optimize the control strategy in a laboratory environment. By optimizing the physical constraints in the diffusion model, the model is ensured to match the physical characteristics of the actual equipment, the reality and reliability of the model are improved, and a more accurate equipment control strategy is obtained. Based on the virtual state distribution diagram of the equipment, multiple rounds of model verification can be performed to continuously iterate and optimize the equipment control strategy to ensure the effectiveness and stability of the strategy. Dynamic control strategies are generated through state matching analysis to ensure that the control strategy can respond to changes in the equipment in real time and optimize the equipment operation efficiency. By coordinating the operation status of multiple devices, conflicts between devices are reduced, and the overall efficiency and resource utilization of the entire production system are improved. Based on the collection and analysis of execution effects, an incremental learning data set is generated, and the equipment control model is continuously optimized to achieve long-term optimization control and improve the continuous stability and efficiency of the equipment. Through differential privacy protection measures, the privacy and security of equipment control data are guaranteed, sensitive data leakage is avoided, and the core technology of the enterprise is protected. Integrity verification of equipment control data can timely detect potential risks of data tampering or corruption, thereby ensuring the accuracy and reliability of the data. According to the security threat monitoring log, the storage and access strategies are dynamically adjusted to ensure the security of equipment control data, enhance the system's ability to resist security attacks, and ensure the safety and reliability of equipment control operations. Therefore, the present invention improves the accuracy and adaptability of intelligent manufacturing equipment control by combining large diffusion models, multimodal data fusion, incremental learning and differential privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a control method for intelligent manufacturing equipment based on a diffusion large model; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a method for controlling intelligent manufacturing equipment based on a diffusion large model, the method comprising the following steps: Step S1: Acquire multimodal data of intelligent manufacturing equipment; perform time series data collection based on the multimodal data of intelligent manufacturing equipment to generate an initial multimodal data set; perform operation mode fusion on the initial multimodal data set to generate multimodal operation data of manufacturing equipment; Step S2: Based on the preset diffusion macro model, the multimodal operation data of the manufacturing equipment is processed for virtual operation to generate a virtual state distribution map of the equipment operation; based on the virtual state distribution map of the equipment operation, the initialized diffusion macro model is optimized for physical constraints to generate a diffusion model matching the physical characteristics of the equipment; based on the virtual state distribution map of the equipment operation, the equipment physical characteristics matching diffusion model is verified and tested for multiple rounds of models to generate an optimized set of equipment control strategies; Step S3: Perform state matching analysis on the multimodal operation data of manufacturing equipment according to the candidate set of equipment control strategies to generate a dynamic control strategy; perform multi-equipment collaborative optimization on intelligent manufacturing equipment based on the dynamic control strategy to generate dynamic equipment collaborative optimization data; perform execution effect collection and analysis on the dynamic equipment collaborative optimization data to generate an incremental learning data set; perform diffusion model incremental learning and continuous optimization on the equipment physical characteristic matching diffusion model through the incremental learning data set, thereby generating a long-term optimization control matrix for the equipment; Step S4: Perform access control on the equipment long-term optimization control matrix and the equipment physical characteristics matching diffusion model to generate equipment control differential privacy protection data; perform integrity verification on the equipment control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access strategy of the equipment long-term optimization control matrix according to the security threat monitoring log to generate a final security optimization strategy to execute intelligent manufacturing equipment control operations.

[0029] The present invention can comprehensively and accurately reflect the operating status of the equipment by acquiring multimodal data (such as temperature, vibration, sound, image, etc.) from multiple sensors. The fusion of multimodal data improves the accuracy of equipment fault detection and status monitoring, and avoids information bias caused by a single data source. Time series data acquisition enables the operation process of the equipment to be dynamically tracked, ensuring timeliness and integrity. Through the fusion of operating modes, the characteristics of different data sources can be integrated to form more comprehensive manufacturing equipment operation data, which helps to improve the quality of subsequent model training and analysis. Equipment virtual operation processing can generate a virtual state distribution map of the equipment, providing a data basis for further physical property analysis and optimization. The physical constraint optimization of the diffusion large model can make the generated model more consistent with the working characteristics of the actual equipment, avoiding the problem of mismatch between the model and the physical characteristics of the equipment in the traditional method. Through multiple rounds of model verification and testing, the accuracy and reliability of the equipment control strategy are ensured, which helps to optimize the control effect and improve the equipment performance. The dynamic control strategy is adjusted according to the real-time operation data of the equipment, ensuring the real-time adaptability and optimization capability of the equipment, and can cope with changes in the equipment under different operating conditions. Multi-device collaborative optimization can simultaneously optimize the working status of multiple devices, improve production efficiency, reduce conflicts between devices, and enhance the collaborative ability of the entire manufacturing system. Through incremental learning, the control model of the device is continuously updated and optimized according to the new execution data, which avoids outdated control strategies and helps to achieve continuous improvement and optimization of the equipment in long-term operation. Differential privacy protection ensures the security of sensitive data in the intelligent manufacturing process, protects the core technology and trade secrets of the enterprise, and prevents data leakage or abuse. Integrity verification of device control data helps to discover potential data tampering risks and ensure the reliability of device control data. Based on the dynamic adjustment of storage and access strategies based on security threat monitoring logs, security strategies can be optimized in real time according to changes in security threats, the overall anti-attack capability of the system can be improved, and the long-term safe and stable operation of intelligent manufacturing equipment can be ensured. Therefore, the present invention improves the accuracy and adaptability of intelligent manufacturing equipment control by combining diffusion large models, multimodal data fusion, incremental learning and differential privacy protection.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for controlling intelligent manufacturing equipment based on a diffusion large model of the present invention. In this example, the method for controlling intelligent manufacturing equipment based on a diffusion large model includes the following steps: Step S1: Acquire multimodal data of intelligent manufacturing equipment; perform time series data collection based on the multimodal data of intelligent manufacturing equipment to generate an initial multimodal data set; perform operation mode fusion on the initial multimodal data set to generate multimodal operation data of manufacturing equipment; In an embodiment of the present invention, by identifying and determining the type of intelligent manufacturing equipment involved, it is ensured that the data modalities provided by different types of equipment are adapted. Intelligent manufacturing equipment includes robotic arms, CNC machine tools, automated assembly lines, robots, etc., and the data collected by them through different sensors will have different modalities. Data from multiple data sources are collected synchronously through a data acquisition system (such as edge computing nodes, IoT platforms, etc.). The acquisition frequency and mode (such as timed acquisition, event-driven acquisition) of each data stream need to be configured according to the actual needs of the device. Add a timestamp to each data stream (such as temperature, pressure, acceleration, etc.) to ensure that each modal data can be aligned at the same time; set a unified time base so that the data of different devices or sensors can be effectively synchronized to avoid data asynchrony due to clock drift, acquisition delay, etc.; considering that the equipment operates under different working conditions, multi-level time granularity acquisition is required (for example, data acquisition of different time granularities according to equipment cycle, operating status, or event triggering, etc.). Use appropriate data acquisition hardware (such as multimodal sensor networks, data acquisition cards) and real-time data processing platforms (such as industrial Internet of Things platforms, edge computing devices) to perform regular data collection to ensure that the collected data meets the timing requirements. Monitor data quality problems that occur during the collection process in real time, such as packet loss, erroneous data, and timing disorder, to ensure that the collected data is complete and valid. Perform necessary preprocessing on the collected multimodal data for subsequent analysis. Common preprocessing steps include: eliminating sensor noise through filtering, smoothing, etc.; interpolating or filling lost data to ensure the integrity of the data set; normalizing or standardizing data of different dimensions (such as temperature, pressure, acceleration, etc.) to keep the data in the same dimension range. Store the preprocessed data in a suitable storage system (such as a database, cloud storage) in a time series order to form an initial multimodal data set. This data set contains data of various modalities and has a unified time label, making subsequent processing and analysis more efficient. If supervised learning of the data is required, labels need to be added to each data point or data segment, such as equipment operating status, fault status, etc. Fuse data of different modalities (such as sensor data, video data, environmental data, etc.) to form a unified, multi-dimensional equipment operation data set. Through this fusion, the operating status and working conditions of the equipment can be more comprehensively reflected. There are differences in the time granularity, data format and feature distribution of different modal data, so they need to be aligned and matched: align the data of all modalities based on the timestamp to ensure that different data in the same time period can correspond to each other; according to specific needs, select the appropriate feature fusion method, such as data-level fusion, feature-level fusion, decision-level fusion, etc. Data-level fusion merges the original data, while feature-level fusion fuses the data after processing.Assign different weights to each modal data, perform weighted averaging, and fuse them into a unified feature; use multimodal neural networks (such as deep neural networks, multi-input convolutional neural networks) for end-to-end feature learning and fusion; use statistical methods such as covariance matrix and principal component analysis (PCA) to reduce the dimension and fuse different modal data. After completing modal fusion, the generated multimodal operation data contains the integration of different modal information and can describe the overall operation of the equipment. At this time, different operating states of the equipment (such as normal operation, critical state, abnormal state, etc.) can be extracted and analyzed from these data. The equipment operation data after multimodal fusion is displayed through a visualization platform (such as a dashboard, 3D graphical interface, etc.), allowing users to monitor the working status of the equipment in real time.

[0031] Step S2: Based on the preset diffusion macro model, the multimodal operation data of the manufacturing equipment is processed for virtual operation to generate a virtual state distribution map of the equipment operation; based on the virtual state distribution map of the equipment operation, the initialized diffusion macro model is optimized for physical constraints to generate a diffusion model matching the physical characteristics of the equipment; based on the virtual state distribution map of the equipment operation, the equipment physical characteristics matching diffusion model is verified and tested for multiple rounds of models to generate an optimized set of equipment control strategies; In an embodiment of the present invention, by selecting a suitable diffusion model (such as a diffusion neural network, a generative adversarial network (GANs), etc.) according to the operating characteristics of the device, the model can process multimodal operating data and simulate the virtual state of the device through the potential characteristics of the data. The role of the diffusion model is to predict the performance of the device under different working conditions by simulating the multimodal operating data of the device. The input data of the model includes the multimodal operating data of the device, such as sensor data, video data, environmental data, etc. The output of the model is the virtual operating state of the device under different working conditions, reflecting various potential behaviors, work efficiency, failure probability, etc. of the device. The multimodal data of the device is virtually simulated using the diffusion model to simulate the performance of the device under different working conditions. This process can help identify the potential operating mode and state of the device, especially when the real device is difficult to implement in an experimental or production environment. Virtual processing can provide important prediction information. The input data flows through the diffusion model, and after multiple layers of processing, the virtual operating state data of the device is generated. Each virtual state represents the working state and behavior performance of the device in different environments. Through the output of the model, a virtual state distribution diagram of the equipment is generated. The diagram shows the state distribution of the equipment under different conditions, reflecting the operating behavior mode of the equipment. This distribution diagram can intuitively show the performance of the equipment under normal, critical and fault conditions. The virtual state diagram is displayed through visualization tools to highlight the changes in the key performance indicators of the equipment (such as temperature, pressure, speed, etc.) under different states. Based on the actual physical characteristics of the equipment, appropriate physical constraints are determined. Physical constraints include the geometric shape, kinematic constraints, dynamic constraints, energy consumption constraints, etc. of the equipment. These constraints reflect the actual physical limitations of the equipment when it is running in a real environment. For example, the maximum load capacity, operating temperature range, maximum speed, etc. of the equipment. Through optimization algorithms (such as constrained optimization, gradient-based optimization, genetic algorithms, etc.), physical constraints are introduced in the training process of the diffusion large model. This can be done by using physical constraints as regularization terms of the model to limit the virtual state generated by the model to not exceed the working range of the actual equipment. Physical constraints can be directly optimized by adding penalty terms to the objective function or by means of constraints, so that the equipment operating state output by the model is more consistent with the real physical environment. After physical constraint optimization, a new equipment model is obtained, which can better match the actual physical characteristics of the equipment. The optimized diffusion model more accurately reflects the operating status of the equipment and can generate virtual operating data that is more in line with reality. In order to ensure the reliability and accuracy of the optimized diffusion model, multiple rounds of verification and testing are required. The verification process includes the following steps: Select a set of representative equipment operation data as the verification set, which should cover different working conditions, loads, faults, etc. Use the verification set to evaluate the optimized diffusion model and check the prediction accuracy of the model under different conditions.Evaluation indicators include prediction error, model robustness, and responsiveness to abnormal conditions. Through multiple rounds of validation tests, the parameters and architecture of the model are gradually optimized. After each round of validation, the model is fine-tuned based on its performance (such as error rate, convergence, etc.). Usually, the validation process involves the following steps: First round of validation: Test the optimized model on a small portion of validation data to analyze its prediction results and errors; Second round of validation: Adjust model parameters and test more data to optimize the generalization ability of the model; Subsequent rounds: Continuously optimize the model through repeated training and testing to ensure its stable performance under different working conditions of the equipment. Comprehensive testing is performed by simulating different equipment working conditions (such as high load, low load, abnormal conditions, etc.) to ensure that the diffusion model can cover all operating modes of the equipment. Test cases may include: equipment operating data under normal conditions; equipment response under critical conditions; simulation of abnormal conditions (such as equipment failure, precursors to failure). Based on the large diffusion model after multiple rounds of validation, the final set of equipment control strategies is generated.

[0032] Step S3: Perform state matching analysis on the multimodal operation data of manufacturing equipment according to the candidate set of equipment control strategies to generate a dynamic control strategy; perform multi-equipment collaborative optimization on intelligent manufacturing equipment based on the dynamic control strategy to generate dynamic equipment collaborative optimization data; perform execution effect collection and analysis on the dynamic equipment collaborative optimization data to generate an incremental learning data set; perform diffusion model incremental learning and continuous optimization on the equipment physical characteristic matching diffusion model through the incremental learning data set, thereby generating a long-term optimization control matrix for the equipment; In an embodiment of the present invention, a candidate set including multiple control strategies is constructed based on the multimodal operation data of the device and the optimization target of the device. Each candidate strategy is predefined according to different working conditions and targets, such as improving production efficiency, reducing energy consumption, and extending equipment life. The candidate set includes different control schemes, algorithms (such as PID control, fuzzy control, predictive control, etc.) and strategy combinations. The operation status of the device is monitored in real time through multimodal data (such as sensor data, environmental data, device feedback, etc.), and state matching analysis is performed on these data. The analysis process includes: sampling and feature extraction of the real-time data of the device (temperature, pressure, speed, vibration, etc.). These state data are matched with candidate control strategies through machine learning methods (such as classifiers, clustering algorithms, etc.) to evaluate which control strategies are most suitable for the current device state. Dynamic time warping (DTW), similarity analysis and other technologies are used to identify and match the current state with the best control strategy in the strategy candidate set. According to the results of the state matching analysis, the control strategy is selected or adaptively adjusted to generate a dynamic, real-time adaptive control strategy. The dynamic control strategy can be adjusted in real time according to the actual operation status, production goals and environmental changes of the device. In the process of intelligent manufacturing, multi-device collaborative optimization refers to achieving the global optimal production efficiency by synchronously controlling the behavior of multiple devices. This process takes into account factors such as the interaction of each device, resource sharing, and task allocation. Based on the dynamic control strategy, multiple devices are combined for collaborative optimization. Build an optimization model whose goal is to minimize the overall production cost, improve production efficiency, or achieve other production goals. Input the operating data, task allocation, and production scheduling information of multiple devices. Use collaborative optimization algorithms (such as cluster optimization, game theory, and distributed optimization) to achieve collaborative operations among multiple devices. The algorithm will achieve global optimization based on the dynamic control strategy of each device. Based on the calculation results of collaborative optimization, generate optimization data containing the specific execution plan of each device in the collaborative optimization process. This data includes the working parameters, task allocation, adjustment strategy, and expected output of the equipment. After the implementation of multi-device collaborative optimization, real-time data collection is carried out to evaluate the execution effect of each device. These data include indicators such as production efficiency, energy consumption, equipment status, and product quality. Through sensors and data acquisition systems, the working status, output data, production efficiency, and fault information of each device are obtained in real time. Through statistical analysis methods, the performance of the equipment after dynamic collaborative optimization is evaluated to detect whether there are deviations or abnormalities. Common evaluation methods include performance comparison analysis, benefit analysis, and risk assessment. Generate an incremental learning dataset based on the analysis results collected from the execution effect. The incremental learning dataset contains new observation data, revised control strategies, and performance feedback data during the equipment execution process. This dataset is the basis for further training of the machine learning model. Using the incremental learning method, the equipment physical characteristics matching diffusion large model is continuously learned and optimized.Incremental learning allows the model to update its parameters each time new data is acquired without retraining the entire model, thereby improving the learning efficiency of the model. Through online learning, incremental update and other methods, newly collected data (such as real-time operation data, collaborative optimization data, etc.) are added to the diffusion model to continuously optimize the operation prediction and control strategy of the equipment. The new data after incremental learning is used to adjust and optimize the parameters of the diffusion model to ensure that the model can better adapt to the physical characteristics of the equipment and predict future equipment behavior. Through continuous optimization, the diffusion model can track the characteristics of long-term changes in the equipment and gradually improve the accuracy of equipment status prediction. Each time the optimization is performed, the model will fit the new data to ensure the long-term and sustainable operation of the equipment. The model is updated and fine-tuned at regular intervals (or according to preset trigger conditions) to ensure that the model can always reflect the latest physical state and operating characteristics of the equipment. Through incremental learning and continuous optimization of the diffusion model, the long-term optimization control matrix of the equipment is finally generated. The matrix contains the optimized control strategy of the equipment under different working conditions, including the best operating parameters, adjustment strategy, collaborative optimization results, etc. The control matrix will form the best control scheme for each equipment under different conditions based on the long-term historical data, execution effect, collaborative optimization results, etc. of the equipment.

[0033] Step S4: Perform access control on the equipment long-term optimization control matrix and the equipment physical characteristics matching diffusion model to generate equipment control differential privacy protection data; perform integrity verification on the equipment control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access strategy of the equipment long-term optimization control matrix according to the security threat monitoring log to generate a final security optimization strategy to execute intelligent manufacturing equipment control operations.

[0034] In the embodiment of the present invention, a refined access rights management mechanism is designed to ensure that only authorized users and devices can access the device long-term optimization control matrix and physical property matching diffusion model. Role-based access control (RBAC) or attribute-based access control (ABAC) models can be used to manage permissions. All access operations must pass multi-factor authentication (such as username / password, fingerprint recognition, dynamic password, etc.) to ensure the authenticity of the identity. At the same time, through detailed permission allocation, it is ensured that different roles or users only access part of their authorized data. On the basis of access control, in order to protect the privacy of device control data, differential privacy technology needs to be adopted. The differential privacy mechanism can ensure that even if the data is accessed or analyzed multiple times, a single data point will not leak sensitive information of individuals or devices. The device control differential privacy protection data is processed, and noise is added to the key data of the control matrix data and the diffusion model to ensure that the original control strategy or device parameters cannot be deduced from the data for external visitors and analysts. The addition of noise should follow the ε-δ mechanism of differential privacy to ensure the strength and operability of privacy protection. Select an appropriate privacy budget (ε) to ensure that the level of privacy protection meets the requirements of relevant regulations (such as GDPR or CCPA) without affecting the effectiveness and accuracy of data analysis. Use a cryptographic hash algorithm (such as SHA-256) to hash the device control differential privacy protection data to generate a unique hash value for the data. Hash verification is required every time the data is read or updated to ensure that the data has not been tampered with. Combined with public key infrastructure (PKI) and digital signature technology, a digital signature is generated for each data change, which can ensure that the data has not been maliciously tampered with during transmission and storage, and provide a basis for subsequent security audits. Set up a security threat monitoring system to track all data access and modification behaviors in real time. The monitoring system can detect abnormal access behaviors (such as illegal access, permission exceeding, etc.) based on technologies such as behavioral analysis and log auditing. When a potential security threat is found, the system automatically generates a detailed threat monitoring log, including timestamp, user identity, access operation, abnormal behavior description, and other information. This log provides an important basis for subsequent security analysis and investigation. Based on the security threat monitoring log, evaluate the storage method of the device's long-term optimization control matrix. If abnormal behavior or potential attacks are found, the control matrix data needs to be migrated to a more secure storage environment (such as encrypted storage, private cloud with restricted access, etc.). Based on risk assessment, adjust the data storage strategy, such as storing sensitive control data in a hierarchical manner, and using different security measures for different levels of control data (such as storing high-security data in restricted hardware devices). Encrypt device control data stored in the cloud or external storage devices to prevent external attackers from obtaining data through illegal means. Adjust access control policies in real time based on security threat monitoring logs.For example, after detecting abnormal access behavior, immediately update access rights or strengthen authentication mechanisms, such as enabling stricter multi-factor authentication and strengthening permission audits. Machine learning algorithms can be combined to make intelligent access policy adjustments based on access patterns and historical security threat logs. For example, identify users who frequently access the device control matrix and dynamically adjust their access rights to ensure the principle of least privilege. Generate the final security optimization strategy based on all data access, storage, privacy protection measures, and monitoring logs. Automatically generate personalized security optimization strategies based on different device types, production requirements, and threat analysis. This can be achieved through a security policy engine or management platform, and dynamically adjust the strategy based on factors such as the device's operating status and usage environment.

[0035] Preferably, step S1 comprises the following steps: Step S11: Acquire multimodal type data of intelligent manufacturing equipment; Step S12: extracting high-frequency sensor signals, operation logs, images and video stream data from the multimodal data of intelligent manufacturing equipment, and performing time series data collection on the high-frequency sensor signals, operation logs, images and video stream data to generate an initial multimodal data set; Step S13: performing time alignment on the initial multimodal data set to generate a multimodal alignment data set for manufacturing equipment; performing inter-modal correction on the multimodal alignment data set for manufacturing equipment to generate a multimodal fusion data set; Step S14: extracting cross-modal features of the multimodal fusion data set to obtain cross-modal feature data of manufacturing equipment, wherein the cross-modal feature data of manufacturing equipment includes frequency domain features, spatial features, and semantic features; fusing the cross-modal feature data of manufacturing equipment to generate a multimodal feature tensor; Step S15: Decompose and compress the multimodal feature tensor to generate multimodal operation data of the manufacturing equipment.

[0036] In an embodiment of the present invention, multimodal data sources of intelligent manufacturing equipment are collected, including but not limited to high-frequency sensor signals, operation logs, images and video stream data, etc. Different sensors and device interfaces are used to obtain real-time data of equipment status, environmental parameters and production processes. The signals collected from different sensors are time-series processed to ensure that various types of data (including sensor signals, operation logs, images and video streams) can be uniformly analyzed on the time axis. For high-frequency sensor signals, the sampling frequency must be high enough to capture fine changes. The operation logs and image video streams are captured synchronously to ensure that various types of data can be effectively coordinated to form an initial multimodal data set. The initial multimodal data set is time-series aligned to ensure that data of different modes are accurately matched in time, so that various types of data correspond to the correct timestamp. On the basis of time-series alignment, inter-modal correction is performed to eliminate the systematic errors of different sensors or data sources, so that each data mode is consistent in information content. After correction, a multimodal alignment data set of manufacturing equipment is generated. Extracting cross-modal features from the multimodal alignment data set mainly includes: extracting the frequency components of the signal through spectrum analysis to help identify the dynamic characteristics of the device. By analyzing the performance of the equipment at different spatial locations, the differences in spatial distribution are extracted. Based on the operation log and image data, semantic information related to the equipment operation status and production process is extracted. These cross-modal features are fused to generate a multimodal feature tensor to represent the overall operation characteristics of the equipment. The generated multimodal feature tensor is decomposed to identify potential low-dimensional feature representations, and data is compressed to reduce redundant information. The compressed multimodal data can be effectively stored and transmitted, and provides a streamlined and efficient input for subsequent equipment operation analysis.

[0037] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Initializing a preset diffusion macromodel based on the multimodal operation data of the manufacturing equipment to generate an initialized diffusion macromodel; inputting the multimodal operation data of the manufacturing equipment into the initialized diffusion macromodel to perform equipment virtual operation processing to generate a virtual state distribution diagram of the equipment operation; Step S22: Based on the equipment operation virtual state distribution diagram, the initialized diffusion model is optimized by physical constraints to generate a diffusion model matching the equipment physical characteristics; the multi-modal operation data of the manufacturing equipment is trained multiple times by using the equipment physical characteristics matching diffusion model to generate a candidate set of equipment control strategies; Step S23: Model verification and testing are performed on the equipment physical characteristic matching diffusion model through the equipment control strategy candidate set to generate an optimized equipment control strategy set.

[0038] In the embodiment of the present invention, the multimodal operation data of the manufacturing equipment, including high-frequency sensor data, operation logs, images and video streams, etc., are obtained as the input of the diffusion model. When the diffusion model is initialized, the equipment operation data is fused with the initial parameters of the model. At this time, the diffusion model can simulate different states and operating environments of the equipment. According to the characteristics of the multimodal operation data of the equipment, the network structure and parameters of the diffusion model are adjusted to adapt to the working mode and working conditions of the equipment. The operation of the equipment is virtually processed using the initialized diffusion model, and the virtual state of the equipment under various working environments and conditions is deduced through the model to generate a distribution diagram of the virtual state of the equipment operation, which shows the operating state of the equipment under different working conditions and the corresponding probability distribution. The distribution diagram of the virtual state of the equipment operation can be used to evaluate the performance of the equipment under different operating conditions and provide data support for subsequent optimization. On the basis of the distribution diagram of the virtual state of the equipment operation, the diffusion model is optimized by introducing the physical constraints of the equipment (such as equipment performance, restrictions, stability requirements, etc.). The goal of physical constraint optimization is to ensure that the equipment model conforms to the physical characteristics of the equipment during the simulation process and avoid the generation of virtual operating states that do not conform to the actual situation. The optimized diffusion model will generate a diffusion model that matches the physical characteristics of the equipment. This model can more accurately simulate the operating status of the equipment in the actual production process. Multiple rounds of training are performed using the equipment physical characteristics matching diffusion model to generate different equipment control strategies based on the model. Each round of training generates a candidate set of equipment control strategies by simulating the operation mode, working conditions and response results of the equipment. These strategies include the optimal operation schemes under different operating modes. The candidate set of control strategies covers a variety of equipment adjustment methods to ensure that optimal control can be achieved under different working conditions. The candidate set of equipment control strategies is applied to the diffusion model for model verification and performance testing. During the model verification process, the effects of different control strategies are evaluated, the responses of the equipment under various working conditions are observed, and their performance is recorded. By comparing the verification results, those control strategies that can improve equipment performance, extend service life, reduce energy consumption or improve production efficiency are selected. Based on the model verification, the best performing control strategies are screened out and combined into an optimized set of equipment control strategies. The optimized control strategy set can dynamically adjust the operation mode of the equipment in actual production and automatically optimize the equipment performance according to different working environments and conditions. These strategies can be implemented through automated control systems to ensure stable operation and efficient production of the equipment during the production process.

[0039] Preferably, performing physical constraint optimization on the initialized diffusion model based on the equipment operation virtual state distribution diagram includes: Extract key operating state parameters from the equipment operation virtual state distribution diagram data to generate equipment operation characteristic matrix data, where the key operating state parameters extracted include operating speed, temperature, and vibration frequency; perform physical constraint modeling on the equipment operation characteristic matrix data to generate physical characteristic constraint function set data; Perform model constraint embedding processing on the physical characteristic constraint function set data to generate preliminary constraint diffusion model data; perform iterative training on the preliminary constraint diffusion model data to generate equipment physical characteristic matching diffusion model training data; Feedback optimization is performed on the equipment physical characteristic matching diffusion model training data to generate the equipment physical characteristic matching diffusion model.

[0040] In an embodiment of the present invention, by extracting key operating state parameters from the virtual state distribution diagram of the equipment operation, these parameters generally include: speed data of the equipment under different working conditions, temperature change data of various parts of the equipment (such as motors, bearings, etc.), and vibration signals of the equipment during operation, which can reflect the dynamic behavior of the equipment. Signal processing technology (such as spectrum analysis, Fourier transform, etc.) is used to extract these important parameters from multimodal data to ensure that the key characteristics of the equipment are captured. The extracted key operating state parameters (operating speed, temperature, vibration frequency) are organized into a matrix form to form equipment operation characteristic matrix data. The equipment operation characteristic matrix data is used to represent the multidimensional characteristics of the equipment under different operating states and can be used as the basis for physical constraint modeling. Based on the physical characteristics of the equipment (such as mechanical limitations, temperature limits, vibration tolerance, etc.), a corresponding physical constraint model is established. The physical constraint modeling process involves the following: according to the maximum operating speed and working condition requirements of the equipment, the speed range is set, according to the thermal management requirements of the equipment, the upper and lower temperature limits are set, and according to the stability and safety requirements of the equipment, the maximum tolerable frequency of vibration is defined. These physical constraint models are converted into constraint functions to form physical characteristic constraint function set data. The generated set of physical characteristic constraint functions is embedded into the diffusion model as the model's constraint conditions. At this point, the model's output not only depends on the input data, but also needs to meet these physical constraints. During the embedding process, an optimization algorithm (such as the Lagrange multiplier method, constraint optimization method, etc.) is used to combine the constraint function with the objective function of the diffusion model to ensure that the model output results meet the physical constraints of the equipment. After the constraint embedding process, the preliminary constraint diffusion model data is generated. At this point, the model can perform equipment state prediction and behavior simulation on the basis of satisfying the physical constraints. The preliminary constraint diffusion model data is used for multiple rounds of iterative training to optimize the model parameters to make it more accurate when simulating equipment behavior. During the training process, it not only depends on the equipment's historical data and virtual state distribution diagram, but also ensures that the model output meets the physical constraints after each round of training. Through continuous training, the model can gradually adapt to the equipment's operating characteristics under different working conditions and generate a more accurate equipment physical characteristic matching diffusion model. In each round of training, a corresponding training data set is generated for further optimization and tuning of the diffusion model. The training data set includes the equipment's operating data, constraints and optimization objectives under various working conditions. Based on the training results, the equipment physical characteristic matching diffusion model is feedback optimized. The feedback optimization process includes the following aspects: Analyze the model's errors in predicting the operating status of the equipment, especially the parts that do not conform to the physical constraints. According to the feedback information, adjust the model parameters and physical constraints to further optimize the accuracy of the model. Use advanced optimization methods (such as gradient descent, genetic algorithm, etc.) to accelerate the convergence speed of model training and avoid overfitting and underfitting. After feedback optimization, a large diffusion model that conforms to the physical characteristics of the equipment is finally generated.The model can accurately simulate the behavior of the equipment under actual working conditions and generate reasonable equipment status predictions under physical constraints. The final model can optimize the equipment control strategy in practical applications and support automatic adjustment and fault prediction in intelligent manufacturing processes.

[0041] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: performing state matching analysis on multi-modal operation data of manufacturing equipment according to the equipment control strategy candidate set to generate a dynamic control strategy; deconstructing a control instruction set based on the dynamic control strategy to generate an equipment control instruction set; Step S32: using the device control instruction set to operate the intelligent manufacturing device and collect feedback data to generate a control execution feedback matrix; adaptively updating the dynamic control strategy in real time through the control execution feedback matrix to generate an adaptive dynamic control strategy; Step S33: performing multi-device collaborative optimization on intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate dynamic equipment collaborative optimization data; performing execution effect collection and analysis on the dynamic equipment collaborative optimization data to generate an incremental learning data set; Step S34: Perform incremental learning and continuous optimization of the diffusion model by matching the physical characteristics of the equipment with the diffusion model through the incremental learning data set, thereby generating a long-term optimization control matrix for the equipment.

[0042] In the embodiment of the present invention, based on the candidate set of device control strategies, the device is subjected to state matching analysis with the multimodal operation data of the manufacturing equipment. A matching algorithm (such as the least squares method, the dynamic time warping algorithm, etc.) is used to evaluate the compatibility between the current operation state of the device and the control strategy, and a state matching evaluation matrix is ​​generated. According to the state matching evaluation results, the control strategies suitable for the current device state are screened and integrated to generate a dynamic control strategy. Based on the dynamic control strategy, the high-level control objectives are decomposed into specific operation steps and instructions. The deconstruction process includes: extracting core control objectives (such as speed regulation, temperature control, fault prevention, etc.). Decomposing into underlying executable device operation instructions (such as setting the speed, adjusting the voltage, performing vibration correction, etc.). Finally, a device control instruction set is generated to provide an instruction basis for subsequent device operations. The device control instruction set is used to operate the intelligent manufacturing equipment and monitor the operation state of the equipment in real time. Feedback data (including operation results, device response, environmental changes, etc.) is collected through multimodal sensors to generate a control execution feedback matrix. According to the control execution feedback matrix, the execution effect of the control strategy is analyzed to evaluate its impact on the equipment performance and operation objectives. Use adaptive algorithms (such as reinforcement learning, adaptive fuzzy control algorithms, etc.) to update dynamic control strategies in real time. Generate adaptive dynamic control strategies through feedback optimization processes (such as adjusting strategy parameters, reallocating control priorities, etc.) to cope with changes in the equipment operating environment. Based on adaptive dynamic control strategies, multiple intelligent manufacturing equipment are collaboratively optimized. Use multi-device collaborative optimization algorithms (such as distributed optimization, collaborative game algorithms, etc.) to coordinate resource allocation, task allocation, and operation rhythm among multiple devices, and generate dynamic equipment collaborative optimization data for evaluating and improving the efficiency of collaboration between devices. The actual performance of equipment collaborative operation is obtained by collecting and analyzing the execution effect of dynamic equipment collaborative optimization data. The analysis content includes equipment collaboration efficiency, operation stability, task completion, etc. The analysis results are organized into incremental learning data sets, which include information such as equipment performance improvement requirements, new operating modes, and uncovered control strategies. Use incremental learning data sets to perform incremental learning on the equipment physical characteristics matching diffusion model. Incremental learning methods include: Gradient accumulation: Update model parameters based on newly added data. Core sample selection: Select samples that contribute the most to model improvement and add them to training. Model regularization: Prevent model overfitting due to incremental learning. On the basis of incremental learning, the prediction ability and generalization performance of the diffusion model are further optimized. The optimization content includes: expanding the physical constraint range of the model to adapt to new working conditions. Improving the model's ability to handle nonlinear characteristics and abnormal conditions, and generating the final long-term optimization control matrix for the equipment.

[0043] Preferably, performing state matching analysis on multimodal operation data of manufacturing equipment according to the equipment control strategy candidate set includes: Screening the operation status rules of the equipment control strategy candidate set to obtain the equipment control operation status rules; performing a comparative analysis of the real-time operation characteristics of the multi-modal operation data of the manufacturing equipment item by item according to the equipment control operation status rules, thereby generating feature comparative analysis data; Calculate the cosine similarity of the feature comparison analysis data to generate a state matching score matrix; extract the highest score from the state matching score matrix to obtain the optimal state matching score data; perform strategy optimization processing on the device control strategy candidate set based on the optimal state matching score data to generate the optimal control strategy set data; Based on the timeline of the multimodal operation data of the manufacturing equipment, the optimal control strategy set data is logically integrated and processed to generate dynamic control strategy data.

[0044] In an embodiment of the present invention, relevant operating status rules are extracted from the candidate set of device control strategies. The operating status rules define the range of equipment operating status to which different strategies are applicable, and generally include the following: speed range (such as low speed, medium speed, high speed). Temperature threshold (such as normal temperature, overheating, overcooling). Vibration frequency level (such as stable, micro-vibration, strong vibration). The goal of screening is to eliminate strategies that are not suitable for the current operating scenario, obtain equipment control operating status rules, and provide constraints for subsequent matching analysis. Extract key operating features from the multimodal operating data of the manufacturing equipment, such as: speed (rotational speed, line speed, etc. measured in real time). Temperature (equipment surface, internal or ambient temperature). Vibration frequency (real-time vibration signal obtained by sensor). Compare and analyze the operating features extracted in real time with the operating status rules item by item. By calculating the feature difference, feature comparison analysis data is generated, which includes the degree of deviation of each operating feature from the rule requirements. Apply the cosine similarity formula to the feature comparison analysis data: ;in, To run the feature vector in real time, is the feature vector of the operation state rule. The calculated cosine similarity represents the matching degree between the current operation feature and each control strategy rule. The matching scores of all strategies are integrated into a state matching score matrix, where each row of the matrix corresponds to a set of equipment operation features and each column corresponds to a control strategy. The highest score and its corresponding strategy of each row are extracted from the state matching score matrix to generate the optimal state matching score data, and the optimal control strategy under the current operation state is identified. According to the optimal state matching score data, the candidate set of equipment control strategies is screened, low-matching strategies are removed, high-matching strategies are retained, and the preferred control strategy set data is generated. Based on the timeline of the multimodal operation data of the manufacturing equipment, the preferred control strategy set is logically integrated. The integration process includes: considering time correlation to ensure the continuity and compatibility between strategies. Eliminating conflicting strategies and giving priority to strategies with wider coverage or stronger applicability. The logically integrated preferred control strategy set is converted into dynamic control strategy data. Dynamic control strategy data is a control instruction framework that adapts to the operation state of the equipment in real time, laying the foundation for subsequent control instruction generation and equipment operation.

[0045] Preferably, step S33 includes the following steps: Step S331: performing inter-device task decomposition processing on the intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate equipment task allocation matrix data, wherein the inter-device task decomposition processing includes load capacity decomposition and resource occupancy decomposition; Step S332: Coordinate the equipment operation parameters of the equipment task allocation matrix data to generate multi-equipment collaborative operation parameter set data; Step S333: dynamically executing simulation processing on the multi-device collaborative operation parameter set data to generate dynamic device collaborative optimization data; extracting key collaborative indicators from the dynamic device collaborative optimization data to obtain multi-device key collaborative indicators, wherein the multi-device key collaborative indicators include inter-device delay, resource conflict or energy consumption deviation; Step S334: collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set.

[0046] In the embodiment of the present invention, the load capacity upper limit and current load state of each device are calculated based on the device performance parameters (such as operating speed, processing capacity, task capacity, etc.). The total amount of tasks is proportionally distributed to different devices to generate load capacity decomposition data. The current resource occupancy of the device (such as CPU usage, memory occupancy, energy consumption, etc.) is evaluated. According to the device resource occupancy status, the task allocation weight is adjusted to optimize the resource allocation efficiency. The load capacity and resource occupancy decomposition data are integrated to generate device task allocation matrix data, which represents the tasks assigned to each device and their priorities. The operating parameter requirements of each device are extracted from the device task allocation matrix, such as task execution time, resource call amount, communication bandwidth, etc. The device operating parameters are adjusted according to the task dependency and the communication requirements between devices to ensure the coordination consistency between devices. The coordination content includes: ensuring the accuracy of the task sequence and timing dependency between devices, unifying the protocol standards for communication between devices, reducing delays, optimizing resource allocation strategies, and avoiding conflicts. The optimized device operation parameter set is output as the input for subsequent simulation and execution. The dynamic behavior of the device in actual operation is simulated by using the collaborative operation parameter set of multiple devices. The simulation process includes: simulation of the execution process after task decomposition, simulation of communication delay and data transmission between multiple devices, and prediction and analysis of energy consumption and resource utilization. Output dynamic device collaborative optimization data to describe the overall performance of device collaborative operation. Extract key collaborative indicators of multiple devices from dynamic device collaborative optimization data, including: the difference in task completion time between devices, the degree of competition between devices for shared resources (such as network bandwidth, storage, etc.), and the difference between actual energy consumption and theoretical energy consumption of devices. Monitor the actual operation data of devices in real time, including task completion rate, device response time, energy consumption data, etc. Compare and analyze the collected operation effect data with dynamic device collaborative optimization data to evaluate the actual effect of the optimization plan. Process the collected execution effect data: extract the difference values ​​of key indicators such as task completion rate, delay, and energy consumption. Analyze the reasons why the collaborative requirements are not met during operation (such as communication bottlenecks, device performance limitations, etc.). Integrate the processed data into an incremental learning data set to provide support for the subsequent optimization of the device physical characteristics matching diffusion model.

[0047] Preferably, step S334 includes the following steps: Step S3341: collect multi-dimensional execution effect data of dynamic device collaborative optimization data to generate an initial data set of device collaborative execution effect; perform expected value comparison analysis on the initial data set of device collaborative execution effect to generate execution deviation data; Step S3342: comparing the execution deviation data with a preset standard execution deviation threshold, and when the execution deviation data is greater than or equal to the preset standard execution deviation threshold, performing attribution analysis and optimization feedback on the execution deviation data to generate execution optimization feedback data; Step S3343: When the execution deviation data is less than a preset standard execution deviation threshold, incremental learning data is constructed for the execution deviation data to generate an incremental learning data set.

[0048] In the embodiment of the present invention, by real-time monitoring and recording of dynamic device collaborative optimization data, multi-dimensional data of device collaborative execution is collected, including: time difference of task completion between devices, actual energy consumption of devices during task execution, bandwidth, storage and computing resource ratio used by devices, and time interval from task allocation to execution completion. The above collected data are integrated to generate an initial data set of device collaborative execution effect. According to the preset performance goals and collaborative requirements, the initial data set of device collaborative execution effect is compared and analyzed to calculate the deviation. The actual task completion time is compared with the expected time, the time deviation is calculated, the actual energy consumption is compared with the expected energy consumption, the energy consumption deviation is calculated, the distribution of resource utilization is statistically calculated, and the resource allocation deviation is calculated. The analysis results are integrated to generate execution deviation data, which indicates the difference between the actual operation effect of the device and the expected value. The execution deviation data is compared with the preset standard execution deviation threshold: it is determined that there is an obvious deviation in the operation of the device and further optimization is required. The operation effect is close to the target and can be used for incremental learning. For the scenario where the deviation exceeds the threshold, the cause of the deviation is analyzed: whether the performance of the device hardware or software meets the standard is analyzed, resource competition or bottleneck problems are identified, the burden distribution of the task allocation strategy on the device is checked, and the communication efficiency between devices is evaluated. Attribution analysis can be achieved through methods such as data visualization, cluster analysis or correlation calculation. Based on the results of attribution analysis, an optimization feedback strategy is generated, including: adjusting task decomposition weights, optimizing resource scheduling schemes, and improving communication protocols or network configurations. Output execution optimization feedback data to provide a reference for adjusting equipment collaborative operation parameters. Filter data with deviations less than the preset standard threshold to extract key feature data in stable operation scenarios. Extract features from the filtered execution deviation data to construct incremental learning samples, including: changing trends in task completion time and response speed, time series distribution of equipment energy consumption, resource occupancy ratio and balance, organize and normalize feature data, and generate incremental learning data sample sets. Summarize feature sample data to build a complete incremental learning data set to provide data support for continuous optimization and long-term learning of equipment physical characteristics matching diffusion models.

[0049] Preferably, step S4 comprises the following steps: Step S41: Distributed encrypted storage is performed on the equipment long-term optimization control matrix and the equipment physical characteristic matching diffusion model to generate an encrypted storage mapping table; access control is performed on the encrypted storage mapping table based on a differential privacy protection mechanism to generate equipment control differential privacy protection data; Step S42: Perform integrity check on the device control differential privacy protection data and generate a security threat monitoring log; dynamically adjust the storage and access strategy of the device long-term optimization control matrix based on the security threat monitoring log, and generate a final security optimization strategy to execute intelligent manufacturing equipment control operations.

[0050] In an embodiment of the present invention, data sharding is performed by matching the device long-term optimization control matrix with the device physical characteristics diffusion model, and the data is divided into multiple sub-data units. According to the distributed storage network, the sub-data units are stored in different nodes to form the physical dispersion of the data. An independent encryption key is assigned to each sub-data unit, and the data is encrypted using an advanced symmetric or asymmetric encryption algorithm (such as AES or RSA) to generate an encrypted storage mapping table of the sub-data unit, wherein the mapping table contains a sub-data unit identifier, a storage node location, and a corresponding encryption key. The mapping table is stored using distributed blockchain technology to ensure that it cannot be tampered with and has high availability. Before accessing the encrypted storage mapping table, differential privacy processing is performed on user requests, and moderate noise is added to protect data sensitivity. The privacy budget of the access request is calculated to ensure that the risk of privacy leakage is controlled within an acceptable range. Access control policies are defined, and access rights are assigned based on user identity authentication and permission levels. The access operation is recorded and monitored in combination with the differential privacy mechanism to generate device control differential privacy protection data. A hash function (such as SHA-256) is used to generate a check value for the device control differential privacy protection data. Compare the hash value of the currently stored data with the hash value of the original record to detect whether the data has been tampered with. If the verification fails, the exception handling process is triggered and recorded in the security threat monitoring log. The security threat monitoring log records the following information: data access time, access user identity, exception type (such as tampering, unauthorized access, etc.), analyzes the log data, and identifies potential security threats. According to the threat type marked in the security threat monitoring log, dynamically adjust the storage and access strategies of the long-term optimization control matrix of the device: adjust the encryption algorithm or key update frequency, limit the access rights of high-risk users, and enhance the access audit frequency. Combine all optimization measures to form a security optimization strategy to ensure the security of the long-term optimization control matrix of the device in distributed storage. The security optimization strategy includes: encryption algorithm update rules, differential privacy budget allocation strategy, and data integrity verification execution frequency.

[0051] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0052] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A control method for intelligent manufacturing equipment based on a diffusion model, characterized in that: The following steps are involved: Step S1: Acquire multimodal type data of intelligent manufacturing equipment; Based on the multimodal data of intelligent manufacturing equipment, time series data is collected to generate an initial multimodal data set; Perform operation mode fusion on the initial multimodal data set to generate multimodal operation data of manufacturing equipment; Step S2: Based on the preset diffusion macro model, the multimodal operation data of the manufacturing equipment is processed for virtual operation to generate a virtual state distribution map of the equipment operation; based on the virtual state distribution map of the equipment operation, the initialized diffusion macro model is optimized for physical constraints to generate a diffusion model matching the physical characteristics of the equipment; based on the virtual state distribution map of the equipment operation, the equipment physical characteristics matching diffusion model is verified and tested for multiple rounds of models to generate an optimized set of equipment control strategies; Step S3: performing state matching analysis on the multimodal operation data of the manufacturing equipment according to the equipment control strategy candidate set to generate a dynamic control strategy; performing multi-equipment collaborative optimization on the intelligent manufacturing equipment based on the dynamic control strategy to generate dynamic equipment collaborative optimization data; Collect and analyze the execution effect of dynamic equipment collaborative optimization data to generate an incremental learning data set; use the incremental learning data set to perform incremental learning and continuous optimization of the diffusion model matching the equipment physical characteristics, thereby generating a long-term optimization control matrix for the equipment; Step S4: Perform access control on the device long-term optimization control matrix and the device physical characteristic matching diffusion model to generate device control differential privacy protection data; Perform integrity check on the differential privacy protection data of device control and generate security threat monitoring logs. Dynamically adjust the storage and access strategies of the equipment long-term optimization control matrix based on the security threat monitoring logs to generate the final security optimization strategy to execute intelligent manufacturing equipment control operations.

2. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire multimodal type data of intelligent manufacturing equipment; Step S12: extracting high-frequency sensor signals, operation logs, images and video stream data from the multimodal data of intelligent manufacturing equipment, and performing time series data collection on the high-frequency sensor signals, operation logs, images and video stream data to generate an initial multimodal data set; Step S13: performing time alignment on the initial multimodal data set to generate a multimodal alignment data set for manufacturing equipment; performing inter-modal correction on the multimodal alignment data set for manufacturing equipment to generate a multimodal fusion data set; Step S14: extracting cross-modal features of the multimodal fusion data set to obtain cross-modal feature data of manufacturing equipment, wherein the cross-modal feature data of manufacturing equipment includes frequency domain features, spatial features, and semantic features; fusing the cross-modal feature data of manufacturing equipment to generate a multimodal feature tensor; Step S15: Decompose and compress the multimodal feature tensor to generate multimodal operation data of the manufacturing equipment.

3. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Initializing a preset diffusion macromodel based on the multimodal operation data of the manufacturing equipment to generate an initialized diffusion macromodel; inputting the multimodal operation data of the manufacturing equipment into the initialized diffusion macromodel to perform equipment virtual operation processing to generate a virtual state distribution diagram of the equipment operation; Step S22: Based on the equipment operation virtual state distribution diagram, the initialized diffusion model is optimized by physical constraints to generate a diffusion model matching the equipment physical characteristics; the multi-modal operation data of the manufacturing equipment is trained multiple times by using the equipment physical characteristics matching diffusion model to generate a candidate set of equipment control strategies; Step S23: Model verification and testing are performed on the equipment physical characteristic matching diffusion model through the equipment control strategy candidate set to generate an optimized equipment control strategy set.

4. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 3 is characterized in that: The physical constraint optimization of the initialized diffusion model based on the virtual state distribution diagram of the equipment operation includes: Extract key operating state parameters from the equipment operation virtual state distribution diagram data to generate equipment operation characteristic matrix data, where the key operating state parameters extracted include operating speed, temperature, and vibration frequency; perform physical constraint modeling on the equipment operation characteristic matrix data to generate physical characteristic constraint function set data; Perform model constraint embedding processing on the physical characteristic constraint function set data to generate preliminary constraint diffusion model data; perform iterative training on the preliminary constraint diffusion model data to generate equipment physical characteristic matching diffusion model training data; Feedback optimization is performed on the equipment physical characteristic matching diffusion model training data to generate the equipment physical characteristic matching diffusion model.

5. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing state matching analysis on multimodal operation data of manufacturing equipment according to the equipment control strategy candidate set to generate a dynamic control strategy; deconstructing a control instruction set based on the dynamic control strategy to generate an equipment control instruction set; Step S32: using the device control instruction set to operate the intelligent manufacturing device and collect feedback data to generate a control execution feedback matrix; adaptively updating the dynamic control strategy in real time through the control execution feedback matrix to generate an adaptive dynamic control strategy; Step S33: performing multi-device collaborative optimization on intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate dynamic equipment collaborative optimization data; performing execution effect collection and analysis on the dynamic equipment collaborative optimization data to generate an incremental learning data set; Step S34: Perform incremental learning and continuous optimization of the diffusion model by matching the physical characteristics of the equipment with the diffusion model through the incremental learning data set, thereby generating a long-term optimization control matrix for the equipment.

6. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 5 is characterized in that: The state matching analysis of the multimodal operation data of manufacturing equipment based on the equipment control strategy candidate set includes: Screening the operation status rules of the equipment control strategy candidate set to obtain the equipment control operation status rules; performing a comparative analysis of the real-time operation characteristics of the multi-modal operation data of the manufacturing equipment item by item according to the equipment control operation status rules, thereby generating feature comparative analysis data; Calculate the cosine similarity of the feature comparison analysis data to generate a state matching score matrix; extract the highest score from the state matching score matrix to obtain the optimal state matching score data; perform strategy optimization processing on the device control strategy candidate set based on the optimal state matching score data to generate the optimal control strategy set data; Based on the timeline of the multimodal operation data of the manufacturing equipment, the optimal control strategy set data is logically integrated and processed to generate dynamic control strategy data.

7. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 5 is characterized in that: Step S33 includes the following steps: Step S331: performing inter-device task decomposition processing on the intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate equipment task allocation matrix data, wherein the inter-device task decomposition processing includes load capacity decomposition and resource occupancy decomposition; Step S332: Coordinate the equipment operation parameters of the equipment task allocation matrix data to generate multi-equipment collaborative operation parameter set data; Step S333: dynamically executing simulation processing on the multi-device collaborative operation parameter set data to generate dynamic device collaborative optimization data; extracting key collaborative indicators from the dynamic device collaborative optimization data to obtain multi-device key collaborative indicators, wherein the multi-device key collaborative indicators include inter-device delay, resource conflict or energy consumption deviation; Step S334: collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set.

8. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 7 is characterized in that: Step S334 includes the following steps: Step S3341: collect multi-dimensional execution effect data of dynamic device collaborative optimization data to generate an initial data set of device collaborative execution effect; perform expected value comparison analysis on the initial data set of device collaborative execution effect to generate execution deviation data; Step S3342: comparing the execution deviation data with a preset standard execution deviation threshold, and when the execution deviation data is greater than or equal to the preset standard execution deviation threshold, performing attribution analysis and optimization feedback on the execution deviation data to generate execution optimization feedback data; Step S3343: When the execution deviation data is less than a preset standard execution deviation threshold, incremental learning data is constructed for the execution deviation data to generate an incremental learning data set.

9. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Distributed encrypted storage is performed on the equipment long-term optimization control matrix and the equipment physical characteristic matching diffusion model to generate an encrypted storage mapping table; access control is performed on the encrypted storage mapping table based on a differential privacy protection mechanism to generate equipment control differential privacy protection data; Step S42: Perform integrity check on the device control differential privacy protection data and generate a security threat monitoring log; dynamically adjust the storage and access strategy of the device long-term optimization control matrix based on the security threat monitoring log, and generate a final security optimization strategy to execute intelligent manufacturing equipment control operations.

10. An intelligent manufacturing equipment control system based on a diffusion large model, characterized in that: Used to execute the intelligent manufacturing equipment control method based on the diffusion large model as claimed in claim 1, the intelligent manufacturing equipment control system based on the diffusion large model includes: The modal fusion module is used to obtain multi-modal data of intelligent manufacturing equipment; perform time series data collection based on the multi-modal data of intelligent manufacturing equipment to generate an initial multi-modal data set; perform operation modal fusion on the initial multi-modal data set to generate multi-modal operation data of manufacturing equipment; The virtual operation module is used to perform virtual operation processing on the multimodal operation data of the manufacturing equipment based on the preset diffusion macro model, and generate a virtual state distribution map of the equipment operation; perform physical constraint optimization on the initialized diffusion macro model based on the equipment operation virtual state distribution map, and generate a diffusion model matching the equipment physical characteristics; perform multiple rounds of model verification and testing on the equipment physical characteristics matching diffusion model based on the equipment operation virtual state distribution map, and generate an optimized set of equipment control strategies; The equipment collaborative control module is used to perform state matching analysis on the multimodal operation data of manufacturing equipment according to the candidate set of equipment control strategies to generate dynamic control strategies; perform multi-equipment collaborative optimization of intelligent manufacturing equipment based on dynamic control strategies to generate dynamic equipment collaborative optimization data; perform execution effect collection and analysis on dynamic equipment collaborative optimization data to generate incremental learning data sets; perform diffusion model incremental learning and continuous optimization on the equipment physical characteristics matching diffusion model through the incremental learning data sets, thereby generating a long-term optimization control matrix for the equipment; The secure storage module is used to perform access control on the equipment long-term optimization control matrix and the equipment physical characteristics matching diffusion model, generate equipment control differential privacy protection data; perform integrity verification on the equipment control differential privacy protection data, and generate security threat monitoring logs; dynamically adjust the storage and access strategies of the equipment long-term optimization control matrix according to the security threat monitoring logs, and generate the final security optimization strategy to execute intelligent manufacturing equipment control operations.

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